Contributed by the Aircraft Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received January 10, 2014; final manuscript received January 14, 2014; published online February 18, 2014. Editor: David Wisler.

Abstract

Gas turbine off design performance prediction is strictly dependent on the accuracy of compressor and turbine map characteristics. Experimental data regarding component maps are very difficult to find in literature, since it is undisclosed proprietary information of the engine manufacturers. To overcome this limitation, gas turbine engineers use available generic component maps and modify them to reach the maximum adherence with the experimental measures. Different scaling and adaptation techniques have been employed to this aim; these methodologies are usually based upon analytic regression models which minimize the deviation from experimental data. However, since these models are built mainly for a specific compressor or turbine map, their generalization is quite difficult: in fact, regression is highly shape-dependent and, therefore, requires a different model for each different specific component. This paper proposes a solution to the problem stated above: a new method for map adaptation is investigated to improve steady-state off design prediction accuracy of a generic gas turbine component. The methodology does not employ analytical regression models; its main principle relies in performing map modifications in an appropriate neighborhood of the multiple experimental points used for the adaptation. When using gas turbine simulation codes, component maps are usually stored in a data matrix and are ordered in a format suitable for 2D interpolation. A perturbation of the values contained in the matrix results in component map morphing. An optimization algorithm varies the perturbation intensity vector in order to minimize the deviation between experimental and predicted points. The adaptation method is integrated inside TSHAFT, the gas turbine prediction code developed at the University of Padova. The assessment of this methodology will be exposed by illustrating a case study carried out upon a turbojet engine.

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